It is often useful to obtain data regarding travel between given origin and destination locations (“an origin-destination pair”). Such data may be used to provide a so-called “origin-destination matrix”, relating to travel between the origin and destination for each of a plurality of different origin-destination pairs. Origin-destination travel data may be obtained reflecting various parameters of travel between an origin and destination pair or pairs. For example, the data may relate to speed of travel or a count of journeys made. Such location-based information may be used in a wide range of applications, including, for example, infrastructure planning, identifying traffic levels associated with certain journeys, and also for the selection of locations, e.g. for commercial premises, advertising purposes, etc.
A basic set of data representing a count of journeys between different origin-destination pairs may be in the form of an origin-destination matrix, containing an array of data representing the count of journeys made between each origin and destination. An origin-destination matrix of this type provides a compact way of storing the relevant data. However, conventional origin-destination matrices suffer from the disadvantage that time related information, such as the time of departure and/or arrival is lost. Techniques for providing origin-destination matrices relating to a count of journeys between origin-destination pairs, and which incorporate information regarding a time dependence of the data, have been proposed, based upon data generated by road-side traffic counters. Such origin-destination matrices may be referred to as “dynamic” matrices, reflecting the time dependence of the data. The time information may be incorporated as a further dimension in the origin-destination matrix. Obtaining data using road-side traffic counters is relatively expensive, and represents an inflexible approach, relying upon the necessary infrastructure to be in place. It can be seen that it may be difficult to obtain extensive data relating to large numbers of origin-destination pairs and different predetermined time periods using data obtained in this manner. Further challenges remain in storing the large quantities of data involved, when time-related information is included.
The Applicant has realised that there remains a need for improved methods of obtaining time dependent information relating to a count of journeys made between different origin-destination pairs, and for products comprising such data.